A similarity of multivariate time series in stocks network analysis

Correlation-based network as a model for financial markets, especially stock market, is a complex system has received much attention. There have been a lot of studies which deals with stocks network analysis, where each stock is represented by a univariate time series of its closing price, and then the similarity between two stocks are quantified by using Pearson correlation coefficient (PCC) on the logarithmic returns. However, in daily stock market activity, stock is represented by a multivariate time series during its opening, highest, lowest, and closing prices. The solely used of the information from closing price may cause the loss of information from other prices. In this thesis, all four prices are considered. The notion of multivariate time series similarity among stocks are developed. The use of Escoufier vector correlation (EVC), a multivariate generalization of PCC, is proposed to measure the similarity between stocks. Then the EVC coefficients are used to construct the stocks network in multivariate setting based on minimal spanning tree (MST). In the case study on BURSA MALAYSIA, the topological properties of stocks in EVC-based MST and in PCC-based MST are different. The total path lengths among stocks in the economic sector according to EVC-based MST is generally smaller than according to PCC-based MST. It means that with the approach of EVC-based MST, the stocks are strongly connected with other stocks in the same sector. Moreover, EVC is proposed to define the similarity between economic sectors, where each sector is represented by a multivariate time series of p components and each component is a univariate time series of stock’s closing price. To the best of our knowledge, there is no previous studies which deals with the similarity between economic sectors using this approach. The methodology for economic sectors network analysis is formulated in this thesis. The current practice of using Kruskal’s or Prim’s algorithm is to obtain MST, and then sub-dominant ultrametric (SDU) from the MST. It will consume a lot of time when the number of stocks is large. Therefore to solve this problem, an efficient algorithm is developed based on fuzzy relation approach. A comparison study based on the empirical and simulated data shows that the proposed algorithm is faster. The proposed algorithm provides not only MST and SDU, but also the forest of all MSTs

Mutation signatures implicate aristolochic acid in bladder cancer development

10.1186/s13073-015-0161-3Genome Medicine71Article number 3

Network topology of Indonesian stock market

This paper proposes a new centrality measure called overall centrality measure that can be used to summarize the important information contained in social network. It is an optimal linear combination of the traditional measures where the optimality criterion is similar to that in principal component analysis. The advantages of this method will be illustrated by using stocks prices in Indonesian Stock Exchange where the relationship among stocks is viewed as social relationship. Some important results will be highlighted

Multivariate time series similarity-based complex network in stocks market analysis: case of NYSE during global crisis 2008

Long before we started with the 21st millennium, Stephen Hawking saw the current millennium as the millennium of complex systems. Until present, he was right due to the fast growing technology in computer. Nowadays, in the era of digital world where big data is our daily menu, we cannot escape from complex systems. As big data is characterized by “4V” (Variety, Velocity, Veracity and Volume), statistics such as practiced in traditional way is not enough and sometime is not apt to be used to understand the most important information contained in big data. What people call now data analytics needs to be used as the only complementary. It is mathematically dominated by multivariate data analysis (MVDA) in the French way. Traditional statistics, which is based on mathematical statistics, is to do confirmatory analysis while data analytics is to do exploratory analysis. The former is to do hypothesis testing (micro analysis) and the latter is for hypothesis generation (macro analysis). Macro analysis is more appropriate to deal with big data. The principal mathematical tool to do macro analysis is MVDA in the French way where big data is considered as a complex system. In this regards, the main problem is to define the similarity among objects of the study such as stocks, economic sectors, currencies, and other commodities in financial industry, which are statistically a multivariate time series. Furthermore, the principal tools to filter the important information contained in a complex system are complex network and social network analysis. To demonstrate the advantages of complex network approach in stocks market analysis, in this paper the behaviour of economic sectors played in NYSE during global crisis in 2008 will be presented and discussed. By nature, all stocks are a multivariate time series. Therefore, in that example, we show that the use of Pearson correlation coefficient is useless to define the similarity among them. We use Escoufier’s vector correlation coefficient instead

A Longitudinal Study of a Capstone Course

This is a 7 years study on a capstone course completed by 1700+ students for 200+ organisations involving 300+ projects. Student teams deliver a system to solve real-world problems proposed by industry partners. We want to understand what independent variables influence student performance. We analysed the deployment status of systems delivered, the type of organization/industry, the number of meetings and the technology used. Our results show some organization value proof of concept over fully deployed systems, student strengths are in InfoComm and Finance projects, the number of meetings is a weak correlation to performance and best performing projects are fully deployed on iOS using Microsoft technologies. Faculty can use these insights to focus on factors such as the type of industry projects and technology used. They may not be overly concern if student teams have fewer meetings or did not fully deployed their system, so long as they create value

POLYMERIC MATERIALS AS PLATFORMS FOR TOPICAL DRUG DELIVERY: A REVIEW

With the emergence of novel and more effective drug therapies, increased importance is being placed upon the drug delivery technology. Topical formulations are attractive alternatives to oral formulations and offer several advantages, such as avoiding first-pass hepatic metabolism and gastric degradation. The major obstacle to drug delivery across the skin (transdermal) is the barrier nature of the skin which limits permeation of molecules. A wide range of polymeric materials is currently available for drug delivery to and across the skin. The synthetic polymers such as polyesters, polyamides, polyurethanes, polyanhydrides and poly(ortho-esters) display advantages of reproducibility of synthesis, a range of material properties and biodegradability, whereas agro-polymers like polysaccharides, proteins and lipids have already shown great promise in terms of type of material, range of properties, processing technique and biocompatibility. This review article summarizes features of different polymers and their potential applications in topical drug delivery system

Impact of pharmacist-delivered interventions on pain-related outcomes: An umbrella review of systematic reviews and meta-analyses

Introduction: Pain is a significant healthcare challenge, impacting millions worldwide. Pharmacists have increasingly taken on expanded roles in managing pain, particularly in primary and ambulatory care contexts. This umbrella review aims to systematically evaluate evidence from published systematic reviews that explore the impact of pharmacist-delivered interventions on clinical, humanistic, and economic outcomes related to pain. Methods: A systematic search was conducted across six electronic databases, including Ovid Embase, MEDLINE, CINAHL, Scopus, CENTRAL, APA PsycINFO, and DARE, from inception until June 2023. Prior to inclusion, two independent reviewers assessed study titles and abstracts. Following inclusion, an assessment of the methodological quality of the included studies was conducted. AMSTAR 2 was used to evaluate the methodological quality of the included SRs. Results: From 2055 retrieved titles, 11 systematic reviews were included, with 5 out of 11 being meta-analyses. These SRs encompassed diverse pharmacist-led interventions such as education, medication reviews, and multi-component strategies targeting various facets of pain management. These findings showed favorable clinical outcomes, including reduced pain intensity, improved medication management, enhanced overall physical and mental well-being, and reduced hospitalization durations. Significant pain intensity reductions were found due to pharmacists' interventions, with standardized mean differences (SMDs) ranging from −0.76 to −0.22 across different studies and subgroups. Physical functioning improvements were observed, with SMDs ranging from −0.38 to 1.03. Positive humanistic outcomes were also reported, such as increased healthcare provider confidence, patient satisfaction, and quality of life (QoL). QoL improvements were reported, with SMDs ranging from 0.29 to 1.03. Three systematic reviews examined pharmacist interventions’ impact on pain-related economic outcomes, highlighting varying cost implications and the need for robust research methodologies to capture costs and benefits. Conclusion: This umbrella review highlights the effectiveness of pharmacist-delivered interventions in improving clinical, humanistic, and economic outcomes related to pain management. Existing evidence emphasises on the need to integrate pharamacists into multi-disciplinary pain management teams. Further research is needed to investigate innovative care models, such as pharmacist-independent prescribing initiatives within collaborative pain management clinics